Shahsavari Shadi, Holur Pavan, Wang Tianyi, Tangherlini Timothy R, Roychowdhury Vwani
Electrical and Computer Engineering, UCLA, Los Angeles, CA USA.
Department of Scandinavian, University of California, Berkeley, CA USA.
J Comput Soc Sci. 2020;3(2):279-317. doi: 10.1007/s42001-020-00086-5. Epub 2020 Oct 28.
Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus's spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.
谣言和阴谋论在信心和信任度较低的环境中甚嚣尘上。因此，鉴于在新冠病毒的传播与控制、或者该疫情对社会和经济的长期影响方面缺乏科学共识，与新冠疫情相关的谣言和阴谋论四处扩散也就不足为奇了。目前在美国社交媒体论坛上流传的说法包括：5G电信网络激活了病毒；疫情是全球阴谋集团炮制的骗局；病毒是中国人故意释放的生物武器；或者比尔·盖茨利用疫情为实施广泛的疫苗接种计划找借口，以推动建立全球监控体系。虽然有些人可能会迅速将这些说法视为对现实世界行为影响不大而不予理会，但最近发生的一些事件，包括手机信号塔被破坏、针对亚裔美国人的种族主义攻击、支持抵制公共卫生指令的示威活动，以及大规模违抗诸如戴口罩和保持社交距离等合理的科学公共指令的行为，都反驳了这种观点。受叙事理论启发，我们爬取社交媒体网站和新闻报道，并通过应用自动化机器学习方法，发现支撑谣言和阴谋论产生的潜在叙事框架。我们展示了助长这些说法的各种叙事框架是如何依赖于原本互不相关的知识领域的结合，并思考它们是如何与关于疫情的更广泛报道相联系的。这些可以近乎实时监测的结合点和联系，可能有助于识别新闻中特别容易被阴谋论者重新解读的领域。了解社交媒体上的叙事动态以及为这些说法提供生成基础的叙事框架，也可能有助于设计出阻止其传播的方法。
